The Unimore RRAM Verilog-A model is a physics-based compact model of bipolar RRAM which includes cycle-to-cycle variability, thermal effects, self-heating, and multilevel Random Telegraph Noise (RTN). The model considers both the quasi-ohmic charge transport along the conductive filament and the trap-assisted tunneling transport in the dielectric barrier. The reset/set operations dynamics is modeled with differential equations considering the field-driven oxygen ions drift and recombination during reset (i.e., barrier growth), and the field accelerated bond breakage during set (i.e., barrier collapse). The temperature dynamics is, likewise, modeled with differential equations that enable accurate predictions also when using very short pulses. Thus, the model enables the advanced design of circuits for many applications such as Memory, Neuromorphic Circuits, RRAM-based Neural Networks, Logic-In-Memory Systems, Physical Unclonable Functions, True Random Number Generators and others.

Unimore Resistive Random Access Memory (RRAM) Verilog-A Model 1.0.0 / Puglisi, Francesco Maria; Zanotti, Tommaso; Pavan, Paolo. - (2019). [10.21981/15GF-KX29]

Unimore Resistive Random Access Memory (RRAM) Verilog-A Model 1.0.0

Francesco Maria Puglisi;ZANOTTI, TOMMASO
;
Paolo Pavan
2019

Abstract

The Unimore RRAM Verilog-A model is a physics-based compact model of bipolar RRAM which includes cycle-to-cycle variability, thermal effects, self-heating, and multilevel Random Telegraph Noise (RTN). The model considers both the quasi-ohmic charge transport along the conductive filament and the trap-assisted tunneling transport in the dielectric barrier. The reset/set operations dynamics is modeled with differential equations considering the field-driven oxygen ions drift and recombination during reset (i.e., barrier growth), and the field accelerated bond breakage during set (i.e., barrier collapse). The temperature dynamics is, likewise, modeled with differential equations that enable accurate predictions also when using very short pulses. Thus, the model enables the advanced design of circuits for many applications such as Memory, Neuromorphic Circuits, RRAM-based Neural Networks, Logic-In-Memory Systems, Physical Unclonable Functions, True Random Number Generators and others.
Puglisi, Francesco Maria; Zanotti, Tommaso; Pavan, Paolo
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1190381
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